Artificial Intelligence(AI) Techniques
Updated on 26th Nov, 22 39 Views

“Predicting the future isn’t magic, it’s artificial intelligence.” – Dave Waters

What is Artificial Intelligence?

A method for educating a computer, a robot controlled by a computer, or software to think critically and creatively like a human mind is known as Artificial Intelligence.

AI can be developed by studying cognitive function. Intelligent systems and software are made available through these research initiatives.

Artificial intelligence (AI) is currently one of the most well-known abbreviations in technology and with good reason. Over the past several years, several advancements and inventions that were formerly only seen in science fiction have started to gradually become reality.

Researchers believe that artificial intelligence is an industrial component that can open up new career choices and transform how work is done in a variety of sectors.

For instance, according to this PWC research, AI may boost the global GDP by $14.9 trillion by 2038. China and the U.S., which together hold about 75% of the world’s influence, are well positioned to gain the most from the impending AI surge.

Table of Contents:

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History of Artificial Intelligence

While artificial intelligence seems to be a hot topic right now, it’s important to keep in mind that this was not a novel concept in 1950. In the same year that Alan Turing devised the Turing test, in 1951, Isaac Asimov proposed the Three Laws of Robotics.

In 1955, the first AI-based software was created. In 1959, the first self-learning video game software was developed. In 1961, the MIT AI lab is founded. The first robot is installed on the GM assembly line in 1964, and the first demonstration of an AI system that can comprehend plain language took place in 1965.

In 1955, the first AI-based software was produced. 1959 saw the creation of the first self-learning video game software. Initiated in 1961, the MIT AI lab In 1964, GM’s assembly line welcomed its first robot. In 1965, a demonstration of an AI software that could comprehend plain language took place.

the first chatbot Eliza first appeared in 1974. At the Stanford AI lab, the first autonomous car is developed in 1989. In 1997, Carnegie Mellon uses a neural network to develop the first autonomous automobile. In 1999, IBM’s Deep Blue chess program defeated Garry Kasparov.

In the same year that Sony releases AIBO, the MIT AI laboratories show off their first emotional AI in 2004. In 2009, DARPA launches its first autonomous vehicle competition. In 2010, Google begins developing a self-driving automobile. IBM Watson defeats the champions of Jeopardy that year. Siri, Google Now, and Cortana have gained popularity.

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A $1 billion commitment to open AI was disclosed by Elon Musk and others in 2016. In 2016, the Korean ‘AlphaGo’ champion is defeated by Google’s deep mind. The 2016 Stanford AI 100 Report is released.

As you can see, AI is not new, but the advancements we are making are moving forward at an exponential rate thanks to our ever-faster computing power, the exponential growth of digital data, and the rapid development of communication equipment.

In some ways, the commercialization era of AI has just begun, and it will profoundly affect our world as we know it in a manner that is at least as profound as the way that the internet has affected us.

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Types of Artificial Intelligence

There are four types of Artificial Intelligence:

Types of Artificial Intelligence
  • Reactive Machines AI
  • Limited Memory AI
  • Theory Of Mind AI
  • Self-aware AI

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Now, we will discuss them in deep.

  • Reactive Machines AI:

This category of AI comprises devices that only use the data that is now available and take into consideration the current circumstances.

Reactive AI systems are unable to conclude from the information to choose the best course of action. They are only capable of a limited set of predetermined duties.

Examples:

  • chess-playing supercomputer
  • spam detection
  • recommendation engine for Netflix
  • Limited Memory AI:

Artificial intelligence is one form that has limited memory. It speaks to an AI’s capacity to retain past information and forecasts and use it to inform future predictions.

The complexity of Machine Learning design increases slightly when memory is constrained. Every ML model needs a little amount of memory to build, but the model may be used as a reactive machine type, as this is the most fundamental and straightforward kind of AI.

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Example:

  • Self-driving automobiles are examples of Limited Memory AI, which leverages data gathered recently to make quick decisions.
  • Theory of Mind AI:

Theories of Mind Artificial intelligence (AI) is a highly developed form of AI. It is thought that this class of devices is crucial to psychology. To better understand human beliefs and ideas, this sort of AI will primarily rely on emotional maturity.

  • Self-Aware AI:

The evolution of AI has reached its conclusion, yet it only exists in theory for now. Self-aware AI, as the name suggests, is an AI that has evolved to be so like the human brain that it has developed self-awareness.

The main goal of all AI research has been and will always be developing this kind of AI, which is decades, if not centuries, away from becoming a reality.

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AI Techniques

Depending on the machine’s ability to utilize past experiences to anticipate future judgments, memory, and self-awareness, artificial intelligence can be classified into a variety of subcategories.

IBM created the chess program Deep Blue, which can recognize the pieces on the chessboard. However, it lacks the memory necessary to anticipate future behavior. Even if this approach is helpful, it cannot be modified for different circumstances.

The decision-making capabilities of self-driving automobiles serve as an illustration of this type of AI system. Here, observations aid in the quick decisions that must be made since they change often and are not permanently preserved.

The development of technology may also make it feasible to create machines with a sense of awareness that can recognize the status of the world and determine what has to be done. But there are no such systems.

Top 4 Techniques of Artificial Intelligence

The top four techniques of AI:

  • Machine Learning:

Machines, in this use of AI, naturally learn from experience rather than being explicitly taught to carry out certain tasks.

Artificial neural networks are the foundation of the subfield of machine learning known as “Deep Learning” that is used for predictive analysis. Unsupervised machine learning, supervised and unsupervised, and reinforcement learning are only a few examples of the many machine learning algorithms.

The algorithm is unsupervised learning and does not employ categorized information to make choices on its own without any direction.

In supervised learning, a feature that includes a combination of an input data set and the intended output is inferred from the training data.

Machines utilize reinforcement learning to determine the best alternative that needs to be considered and to take the appropriate actions to improve the reward.

  • Machine Vision:

Machines are capable of collecting and analyzing visual data. In this case, cameras are utilized to record sensory information, which is then processed using digital signal processing once the picture is converted from analog to digital.

The data that is produced is then input into a computer. Sensitivity is the capacity of the machine to recognize weak impulses and resolution.    The extent to which it can discriminate between objects—are two essential components of machine vision.

Machine vision is used in a variety of applications, including object recognition, medical picture analysis, and signature detection.

  • NLP( Natural Language Processing)

The way in which computers were trained to comprehend natural languages is via their connections with human language.

Natural Language Processing, the method of extracting meaning from human languages, is trustworthy technology. The machine in NLP records the speech of a person speaking.

Following the audio-to-textual dialogue, the writing is converted to turn the data into audio.

The system then responds to people via audio. Applications of NLP may be found in Interactive Voice Response (IVR) systems used in contact centers, in language translations like Google Translate, and in word processors that verify the correctness of syntax in text, like Microsoft Word.

  • Automation and Robotics

The goal of automation is to enable machines to perform boring, repetitive jobs, increasing productivity and delivering more effective, efficient, and affordable results. In order to automate processes, many businesses employ machine learning, artificial neural, and graphs.

By leveraging the CAPTCHA technique, this automation can avoid fraud problems during online payments.

Robotic process automation is designed to carry out high-volume, repetitive jobs while being capable of adapting to changing conditions.

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Future of Artificial Intelligence

Artificial intelligence (AI) is unquestionably a cutting-edge area of computer science that is poised to dominate a number of burgeoning industries, including data science, robots, and the internet of things. In the upcoming years, this will keep on innovating in the field of technology.

Artificial Intelligence has gone from science fiction to reality in just a few years. Intelligent machines that assist humans exist in real life as well as in science fiction films. We now inhabit a universe of a.i., which was only a tale a few years ago.

Whether we are conscious of it or not, artificial intelligence technology is now engrained in our society and is employed in our daily activities. Nowadays, everyone makes use of AI in their daily lives, from chatbots to Alexa and Siri.

This technology-based industry is advancing and changing. But it wasn’t as easy and simple as it seemed to us. It has taken a significant amount of work and the participation of many people to develop AI to this stage.

Conclusion

Everything related to artificial intelligence technologies being at the core of a new endeavor to create computer models of intelligence has been covered in this blog. The fundamental premise is that any form of intelligence, whether human or nonhuman, may be expressed in terms of symbol structures and symbolic operations that can be coded in a digital computer.

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